Pandas Indexes & Sorting¶

Index Basics¶

In [2]:

import pandas as pdbtc = pd.read_csv("data/coin_Bitcoin.csv")

In [3]:

btc

Out[3]:

	SNo	Name	Symbol	Date	High	Low	Open	Close	Volume	Marketcap
0	1	Bitcoin	BTC	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2	Bitcoin	BTC	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	3	Bitcoin	BTC	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	4	Bitcoin	BTC	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	5	Bitcoin	BTC	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...	...
2986	2987	Bitcoin	BTC	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2988	Bitcoin	BTC	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2989	Bitcoin	BTC	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2990	Bitcoin	BTC	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2991	Bitcoin	BTC	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 10 columns

In [4]:

btc.set_index("Date")

Out[4]:

	SNo	Name	Symbol	High	Low	Open	Close	Volume	Marketcap
Date
2013-04-29 23:59:59	1	Bitcoin	BTC	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
2013-04-30 23:59:59	2	Bitcoin	BTC	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2013-05-01 23:59:59	3	Bitcoin	BTC	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
2013-05-02 23:59:59	4	Bitcoin	BTC	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
2013-05-03 23:59:59	5	Bitcoin	BTC	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...
2021-07-02 23:59:59	2987	Bitcoin	BTC	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2021-07-03 23:59:59	2988	Bitcoin	BTC	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2021-07-04 23:59:59	2989	Bitcoin	BTC	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2021-07-05 23:59:59	2990	Bitcoin	BTC	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2021-07-06 23:59:59	2991	Bitcoin	BTC	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 9 columns

In [5]:

btc

Out[5]:

	SNo	Name	Symbol	Date	High	Low	Open	Close	Volume	Marketcap
0	1	Bitcoin	BTC	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2	Bitcoin	BTC	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	3	Bitcoin	BTC	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	4	Bitcoin	BTC	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	5	Bitcoin	BTC	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...	...
2986	2987	Bitcoin	BTC	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2988	Bitcoin	BTC	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2989	Bitcoin	BTC	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2990	Bitcoin	BTC	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2991	Bitcoin	BTC	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 10 columns

In [6]:

btc.set_index("Date", inplace=True)

In [7]:

btc

Out[7]:

	SNo	Name	Symbol	High	Low	Open	Close	Volume	Marketcap
Date
2013-04-29 23:59:59	1	Bitcoin	BTC	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
2013-04-30 23:59:59	2	Bitcoin	BTC	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2013-05-01 23:59:59	3	Bitcoin	BTC	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
2013-05-02 23:59:59	4	Bitcoin	BTC	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
2013-05-03 23:59:59	5	Bitcoin	BTC	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...
2021-07-02 23:59:59	2987	Bitcoin	BTC	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2021-07-03 23:59:59	2988	Bitcoin	BTC	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2021-07-04 23:59:59	2989	Bitcoin	BTC	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2021-07-05 23:59:59	2990	Bitcoin	BTC	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2021-07-06 23:59:59	2991	Bitcoin	BTC	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 9 columns

In [8]:

btc.index

Out[8]:

Index(['2013-04-29 23:59:59', '2013-04-30 23:59:59', '2013-05-01 23:59:59',

'2013-05-02 23:59:59', '2013-05-03 23:59:59', '2013-05-04 23:59:59',

'2013-05-05 23:59:59', '2013-05-06 23:59:59', '2013-05-07 23:59:59',

'2013-05-08 23:59:59',

...

'2021-06-27 23:59:59', '2021-06-28 23:59:59', '2021-06-29 23:59:59',

'2021-06-30 23:59:59', '2021-07-01 23:59:59', '2021-07-02 23:59:59',

'2021-07-03 23:59:59', '2021-07-04 23:59:59', '2021-07-05 23:59:59',

'2021-07-06 23:59:59'],

dtype='object', name='Date', length=2991)

In [9]:

btc.High

Out[9]:

Date

2013-04-29 23:59:59 147.488007

2013-04-30 23:59:59 146.929993

2013-05-01 23:59:59 139.889999

2013-05-02 23:59:59 125.599998

2013-05-03 23:59:59 108.127998

...

2021-07-02 23:59:59 33939.588699

2021-07-03 23:59:59 34909.259899

2021-07-04 23:59:59 35937.567147

2021-07-05 23:59:59 35284.344430

2021-07-06 23:59:59 35038.536363

Name: High, Length: 2991, dtype: float64

In [10]:

btc.High.plot()

Out[10]:

<AxesSubplot:xlabel='Date'>

In [11]:

btc = pd.read_csv("data/coin_Bitcoin.csv")btc.High.plot()

Out[11]:

<AxesSubplot:>

In [12]:

countries = pd.read_csv("data/world-happiness-report-2021.csv")

In [73]:

countries["Healthy life expectancy"]

Out[73]:

Country name

Afghanistan 52.493

Albania 68.999

Algeria 66.005

Argentina 69.000

Armenia 67.055

...

Venezuela 66.700

Vietnam 68.034

Yemen 57.122

Zambia 55.809

Zimbabwe 56.201

Name: Healthy life expectancy, Length: 149, dtype: float64

In [14]:

countries

Out[14]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
0	Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
1	Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
3	Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
4	Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
145	Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
146	Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
147	Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
148	Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 20 columns

In [15]:

countries.set_index("Country name", inplace=True)

In [16]:

countries

Out[16]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [17]:

countries["Healthy life expectancy"]

Out[17]:

Country name

Finland 72.000

Denmark 72.700

Switzerland 74.400

Iceland 73.000

Netherlands 72.400

...

Lesotho 48.700

Botswana 59.269

Rwanda 61.400

Zimbabwe 56.201

Afghanistan 52.493

Name: Healthy life expectancy, Length: 149, dtype: float64

In [18]:

countries["Healthy life expectancy"].head(5).plot()

Out[18]:

<AxesSubplot:xlabel='Country name'>

In [19]:

df = pd.read_csv("data/world-happiness-report-2021.csv", index_col=0)

In [20]:

Out[20]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [21]:

df["Ladder score"]

Out[21]:

Country name

Finland 7.842

Denmark 7.620

Switzerland 7.571

Iceland 7.554

Netherlands 7.464

...

Lesotho 3.512

Botswana 3.467

Rwanda 3.415

Zimbabwe 3.145

Afghanistan 2.523

Name: Ladder score, Length: 149, dtype: float64

In [22]:

countries

Out[22]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

Sorting¶

In [23]:

countries.sort_values("Healthy life expectancy")

Out[23]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379

149 rows × 19 columns

In [24]:

countries.sort_values("Healthy life expectancy", ascending=False)

Out[24]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209

149 rows × 19 columns

In [25]:

countries

Out[25]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [26]:

countries.sort_values("Healthy life expectancy", ascending=False, inplace=True)

In [27]:

countries

Out[27]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209

149 rows × 19 columns

In [28]:

houses = pd.read_csv("data/kc_house_data.csv")titanic = pd.read_csv("data/titanic.csv")

In [29]:

houses.sort_values("price", ascending=False)

Out[29]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
7252	6762700020	20141013T000000	7700000.0	6	8.00	12050	27600	2.5	0	3	...	13	8570	3480	1910	1987	98102	47.6298	-122.323	3940	8800
3914	9808700762	20140611T000000	7062500.0	5	4.50	10040	37325	2.0	1	2	...	11	7680	2360	1940	2001	98004	47.6500	-122.214	3930	25449
9254	9208900037	20140919T000000	6885000.0	6	7.75	9890	31374	2.0	0	4	...	13	8860	1030	2001	0	98039	47.6305	-122.240	4540	42730
4411	2470100110	20140804T000000	5570000.0	5	5.75	9200	35069	2.0	0	0	...	13	6200	3000	2001	0	98039	47.6289	-122.233	3560	24345
1448	8907500070	20150413T000000	5350000.0	5	5.00	8000	23985	2.0	0	4	...	12	6720	1280	2009	0	98004	47.6232	-122.220	4600	21750
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8274	3883800011	20141105T000000	82000.0	3	1.00	860	10426	1.0	0	0	...	6	860	0	1954	0	98146	47.4987	-122.341	1140	11250
16198	3028200080	20150324T000000	81000.0	2	1.00	730	9975	1.0	0	0	...	5	730	0	1943	0	98168	47.4808	-122.315	860	9000
465	8658300340	20140523T000000	80000.0	1	0.75	430	5050	1.0	0	0	...	4	430	0	1912	0	98014	47.6499	-121.909	1200	7500
15293	40000362	20140506T000000	78000.0	2	1.00	780	16344	1.0	0	0	...	5	780	0	1942	0	98168	47.4739	-122.280	1700	10387
1149	3421079032	20150217T000000	75000.0	1	0.00	670	43377	1.0	0	0	...	3	670	0	1966	0	98022	47.2638	-121.906	1160	42882

21613 rows × 21 columns

In [30]:

houses.sort_values("bedrooms", ascending=False)

Out[30]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
15870	2402100895	20140625T000000	640000.0	33	1.75	1620	6000	1.0	0	0	...	7	1040	580	1947	0	98103	47.6878	-122.331	1330	4700
8757	1773100755	20140821T000000	520000.0	11	3.00	3000	4960	2.0	0	0	...	7	2400	600	1918	1999	98106	47.5560	-122.363	1420	4960
15161	5566100170	20141029T000000	650000.0	10	2.00	3610	11914	2.0	0	0	...	7	3010	600	1958	0	98006	47.5705	-122.175	2040	11914
13314	627300145	20140814T000000	1148000.0	10	5.25	4590	10920	1.0	0	2	...	9	2500	2090	2008	0	98004	47.5861	-122.113	2730	10400
19254	8812401450	20141229T000000	660000.0	10	3.00	2920	3745	2.0	0	0	...	7	1860	1060	1913	0	98105	47.6635	-122.320	1810	3745
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19452	3980300371	20140926T000000	142000.0	0	0.00	290	20875	1.0	0	0	...	1	290	0	1963	0	98024	47.5308	-121.888	1620	22850
8484	2310060040	20140925T000000	240000.0	0	2.50	1810	5669	2.0	0	0	...	7	1810	0	2003	0	98038	47.3493	-122.053	1810	5685
875	6306400140	20140612T000000	1095000.0	0	0.00	3064	4764	3.5	0	2	...	7	3064	0	1990	0	98102	47.6362	-122.322	2360	4000
8477	2569500210	20141117T000000	339950.0	0	2.50	2290	8319	2.0	0	0	...	8	2290	0	1985	0	98042	47.3473	-122.151	2500	8751
9773	3374500520	20150429T000000	355000.0	0	0.00	2460	8049	2.0	0	0	...	8	2460	0	1990	0	98031	47.4095	-122.168	2520	8050

21613 rows × 21 columns

In [31]:

houses.sort_values(["bedrooms", "bathrooms"], ascending=False)

Out[31]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
15870	2402100895	20140625T000000	640000.0	33	1.75	1620	6000	1.0	0	0	...	7	1040	580	1947	0	98103	47.6878	-122.331	1330	4700
8757	1773100755	20140821T000000	520000.0	11	3.00	3000	4960	2.0	0	0	...	7	2400	600	1918	1999	98106	47.5560	-122.363	1420	4960
13314	627300145	20140814T000000	1148000.0	10	5.25	4590	10920	1.0	0	2	...	9	2500	2090	2008	0	98004	47.5861	-122.113	2730	10400
19254	8812401450	20141229T000000	660000.0	10	3.00	2920	3745	2.0	0	0	...	7	1860	1060	1913	0	98105	47.6635	-122.320	1810	3745
15161	5566100170	20141029T000000	650000.0	10	2.00	3610	11914	2.0	0	0	...	7	3010	600	1958	0	98006	47.5705	-122.175	2040	11914
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6994	2954400190	20140624T000000	1295650.0	0	0.00	4810	28008	2.0	0	0	...	12	4810	0	1990	0	98053	47.6642	-122.069	4740	35061
9773	3374500520	20150429T000000	355000.0	0	0.00	2460	8049	2.0	0	0	...	8	2460	0	1990	0	98031	47.4095	-122.168	2520	8050
9854	7849202190	20141223T000000	235000.0	0	0.00	1470	4800	2.0	0	0	...	7	1470	0	1996	0	98065	47.5265	-121.828	1060	7200
14423	9543000205	20150413T000000	139950.0	0	0.00	844	4269	1.0	0	0	...	7	844	0	1913	0	98001	47.2781	-122.250	1380	9600
19452	3980300371	20140926T000000	142000.0	0	0.00	290	20875	1.0	0	0	...	1	290	0	1963	0	98024	47.5308	-121.888	1620	22850

21613 rows × 21 columns

In [32]:

houses.sort_values(["bathrooms", "bedrooms"], ascending=False)

Out[32]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
12777	1225069038	20140505T000000	2280000.0	7	8.00	13540	307752	3.0	0	4	...	12	9410	4130	1999	0	98053	47.6675	-121.986	4850	217800
7252	6762700020	20141013T000000	7700000.0	6	8.00	12050	27600	2.5	0	3	...	13	8570	3480	1910	1987	98102	47.6298	-122.323	3940	8800
9254	9208900037	20140919T000000	6885000.0	6	7.75	9890	31374	2.0	0	4	...	13	8860	1030	2001	0	98039	47.6305	-122.240	4540	42730
8546	424049043	20140811T000000	450000.0	9	7.50	4050	6504	2.0	0	0	...	7	4050	0	1996	0	98144	47.5923	-122.301	1448	3866
4024	9175600025	20141007T000000	800000.0	7	6.75	7480	41664	2.0	0	2	...	11	5080	2400	1953	0	98166	47.4643	-122.368	2810	33190
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6994	2954400190	20140624T000000	1295650.0	0	0.00	4810	28008	2.0	0	0	...	12	4810	0	1990	0	98053	47.6642	-122.069	4740	35061
9773	3374500520	20150429T000000	355000.0	0	0.00	2460	8049	2.0	0	0	...	8	2460	0	1990	0	98031	47.4095	-122.168	2520	8050
9854	7849202190	20141223T000000	235000.0	0	0.00	1470	4800	2.0	0	0	...	7	1470	0	1996	0	98065	47.5265	-121.828	1060	7200
14423	9543000205	20150413T000000	139950.0	0	0.00	844	4269	1.0	0	0	...	7	844	0	1913	0	98001	47.2781	-122.250	1380	9600
19452	3980300371	20140926T000000	142000.0	0	0.00	290	20875	1.0	0	0	...	1	290	0	1963	0	98024	47.5308	-121.888	1620	22850

21613 rows × 21 columns

In [33]:

titanic

Out[33]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	?	C	?	328	?
1305	3	0	Zabour, Miss. Thamine	female	?	1	0	2665	14.4542	?	C	?	?	?
1306	3	0	Zakarian, Mr. Mapriededer	male	26.5	0	0	2656	7.225	?	C	?	304	?
1307	3	0	Zakarian, Mr. Ortin	male	27	0	0	2670	7.225	?	C	?	?	?
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?

1309 rows × 14 columns

In [34]:

titanic.sort_values("name")

Out[34]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
600	3	0	Abbing, Mr. Anthony	male	42	0	0	C.A. 5547	7.55	?	S	?	?	?
601	3	0	Abbott, Master. Eugene Joseph	male	13	0	2	C.A. 2673	20.25	?	S	?	?	East Providence, RI
602	3	0	Abbott, Mr. Rossmore Edward	male	16	1	1	C.A. 2673	20.25	?	S	?	190	East Providence, RI
603	3	1	Abbott, Mrs. Stanton (Rosa Hunt)	female	35	1	1	C.A. 2673	20.25	?	S	A	?	East Providence, RI
604	3	1	Abelseth, Miss. Karen Marie	female	16	0	0	348125	7.65	?	S	16	?	Norway Los Angeles, CA
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
392	2	1	del Carlo, Mrs. Sebastiano (Argenia Genovesi)	female	24	1	0	SC/PARIS 2167	27.7208	?	C	12	?	Lucca, Italy / California
1262	3	0	van Billiard, Master. James William	male	?	1	1	A/5. 851	14.5	?	S	?	?	?
1263	3	0	van Billiard, Master. Walter John	male	11.5	1	1	A/5. 851	14.5	?	S	?	1	?
1264	3	0	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	14.5	?	S	?	255	?
1268	3	0	van Melkebeke, Mr. Philemon	male	?	0	0	345777	9.5	?	S	?	?	?

1309 rows × 14 columns

In [35]:

titanic.sort_values("name",key=lambda col: col.str.lower())

Out[35]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
600	3	0	Abbing, Mr. Anthony	male	42	0	0	C.A. 5547	7.55	?	S	?	?	?
601	3	0	Abbott, Master. Eugene Joseph	male	13	0	2	C.A. 2673	20.25	?	S	?	?	East Providence, RI
602	3	0	Abbott, Mr. Rossmore Edward	male	16	1	1	C.A. 2673	20.25	?	S	?	190	East Providence, RI
603	3	1	Abbott, Mrs. Stanton (Rosa Hunt)	female	35	1	1	C.A. 2673	20.25	?	S	A	?	East Providence, RI
604	3	1	Abelseth, Miss. Karen Marie	female	16	0	0	348125	7.65	?	S	16	?	Norway Los Angeles, CA
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	?	C	?	328	?
1305	3	0	Zabour, Miss. Thamine	female	?	1	0	2665	14.4542	?	C	?	?	?
1306	3	0	Zakarian, Mr. Mapriededer	male	26.5	0	0	2656	7.225	?	C	?	304	?
1307	3	0	Zakarian, Mr. Ortin	male	27	0	0	2670	7.225	?	C	?	?	?
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?

1309 rows × 14 columns

In [36]:

countries

Out[36]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209

149 rows × 19 columns

In [37]:

countries.sort_index()

Out[37]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Algeria	Middle East and North Africa	4.887	0.053	4.991	4.783	9.342	0.802	66.005	0.480	-0.067	0.752	2.43	0.946	0.765	0.552	0.119	0.144	0.120	2.242
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Armenia	Commonwealth of Independent States	5.283	0.058	5.397	5.168	9.487	0.799	67.055	0.825	-0.168	0.629	2.43	0.996	0.758	0.585	0.540	0.079	0.198	2.127
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205

149 rows × 19 columns

In [38]:

countries.sort_index(ascending=False, inplace=True)

In [39]:

countries

Out[39]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Armenia	Commonwealth of Independent States	5.283	0.058	5.397	5.168	9.487	0.799	67.055	0.825	-0.168	0.629	2.43	0.996	0.758	0.585	0.540	0.079	0.198	2.127
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Algeria	Middle East and North Africa	4.887	0.053	4.991	4.783	9.342	0.802	66.005	0.480	-0.067	0.752	2.43	0.946	0.765	0.552	0.119	0.144	0.120	2.242
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [40]:

titanic.sort_index(ascending=False)

Out[40]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?
1307	3	0	Zakarian, Mr. Ortin	male	27	0	0	2670	7.225	?	C	?	?	?
1306	3	0	Zakarian, Mr. Mapriededer	male	26.5	0	0	2656	7.225	?	C	?	304	?
1305	3	0	Zabour, Miss. Thamine	female	?	1	0	2665	14.4542	?	C	?	?	?
1304	3	0	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	?	C	?	328	?
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO

1309 rows × 14 columns

In [41]:

titanic.pclass.value_counts().sort_values()

Out[41]:

2 277

1 323

3 709

Name: pclass, dtype: int64

In [42]:

titanic.pclass.value_counts().plot(kind="bar")

Out[42]:

<AxesSubplot:>

In [43]:

titanic.pclass.value_counts().sort_values().plot(kind="bar")

Out[43]:

<AxesSubplot:>

In [44]:

titanic.pclass.value_counts().sort_index().plot(kind="bar")

Out[44]:

<AxesSubplot:>

In [45]:

houses.bedrooms.value_counts().sort_values().plot(kind="bar")

Out[45]:

<AxesSubplot:>

In [46]:

houses.bedrooms.value_counts().sort_index().plot(kind="bar")

Out[46]:

<AxesSubplot:>

In [47]:

countries.head()

Out[47]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135

In [74]:

countries["Ladder score"]

Out[74]:

Country name

Afghanistan 2.523

Albania 5.117

Algeria 4.887

Argentina 5.929

Armenia 5.283

...

Venezuela 4.892

Vietnam 5.411

Yemen 3.658

Zambia 4.073

Zimbabwe 3.145

Name: Ladder score, Length: 149, dtype: float64

loc[] & iloc[]¶

In [49]:

countries.loc["Yemen"]

Out[49]:

Regional indicator Middle East and North Africa

Ladder score 3.658

Standard error of ladder score 0.07

upperwhisker 3.794

lowerwhisker 3.521

Logged GDP per capita 7.578

Social support 0.832

Healthy life expectancy 57.122

Freedom to make life choices 0.602

Generosity -0.147

Perceptions of corruption 0.8

Ladder score in Dystopia 2.43

Explained by: Log GDP per capita 0.329

Explained by: Social support 0.831

Explained by: Healthy life expectancy 0.272

Explained by: Freedom to make life choices 0.268

Explained by: Generosity 0.092

Explained by: Perceptions of corruption 0.089

Dystopia + residual 1.776

Name: Yemen, dtype: object

In [50]:

countries.loc[["Yemen"]]

Out[50]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Yemen	Middle East and North Africa	3.658	0.07	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.8	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776

In [51]:

countries.loc[["Canada"]]

Out[51]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Canada	North America and ANZ	7.103	0.042	7.185	7.021	10.776	0.926	73.8	0.915	0.089	0.415	2.43	1.447	1.044	0.798	0.648	0.246	0.335	2.585

In [52]:

countries.loc[["Canada", "Mexico", "United States"]]

Out[52]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Canada	North America and ANZ	7.103	0.042	7.185	7.021	10.776	0.926	73.800	0.915	0.089	0.415	2.43	1.447	1.044	0.798	0.648	0.246	0.335	2.585
Mexico	Latin America and Caribbean	6.317	0.053	6.420	6.213	9.859	0.831	68.597	0.862	-0.147	0.799	2.43	1.126	0.830	0.634	0.585	0.092	0.089	2.961
United States	North America and ANZ	6.951	0.049	7.047	6.856	11.023	0.920	68.200	0.837	0.098	0.698	2.43	1.533	1.030	0.621	0.554	0.252	0.154	2.807

In [53]:

titanic.loc[[7,9,876]]

Out[53]:

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

Andrews, Mr. Thomas Jr

male

112050

A36

Belfast, NI

Artagaveytia, Mr. Ramon

male

PC 17609

49.5042

Montevideo, Uruguay

876

Ilieff, Mr. Ylio

male

349220

7.8958

In [54]:

titanic.loc[5:10]

Out[54]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
5	1	1	Anderson, Mr. Harry	male	48	0	0	19952	26.55	E12	S	3	?	New York, NY
6	1	1	Andrews, Miss. Kornelia Theodosia	female	63	1	0	13502	77.9583	D7	S	10	?	Hudson, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39	0	0	112050	0	A36	S	?	?	Belfast, NI
8	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53	2	0	11769	51.4792	C101	S	D	?	Bayside, Queens, NY
9	1	0	Artagaveytia, Mr. Ramon	male	71	0	0	PC 17609	49.5042	?	C	?	22	Montevideo, Uruguay
10	1	0	Astor, Col. John Jacob	male	47	1	0	PC 17757	227.525	C62 C64	C	?	124	New York, NY

In [55]:

titanic.loc[5:10:2]

Out[55]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
5	1	1	Anderson, Mr. Harry	male	48	0	0	19952	26.55	E12	S	3	?	New York, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39	0	0	112050	0	A36	S	?	?	Belfast, NI
9	1	0	Artagaveytia, Mr. Ramon	male	71	0	0	PC 17609	49.5042	?	C	?	22	Montevideo, Uruguay

In [56]:

countries.sort_index(inplace=True)

In [57]:

countries

Out[57]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Algeria	Middle East and North Africa	4.887	0.053	4.991	4.783	9.342	0.802	66.005	0.480	-0.067	0.752	2.43	0.946	0.765	0.552	0.119	0.144	0.120	2.242
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Armenia	Commonwealth of Independent States	5.283	0.058	5.397	5.168	9.487	0.799	67.055	0.825	-0.168	0.629	2.43	0.996	0.758	0.585	0.540	0.079	0.198	2.127
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205

149 rows × 19 columns

In [58]:

countries.loc["Denmark":"France"]

Out[58]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Dominican Republic	Latin America and Caribbean	5.545	0.071	5.685	5.405	9.802	0.853	66.102	0.860	-0.133	0.714	2.43	1.106	0.879	0.555	0.581	0.101	0.144	2.178
Ecuador	Latin America and Caribbean	5.764	0.057	5.875	5.653	9.313	0.821	68.800	0.842	-0.124	0.843	2.43	0.935	0.806	0.640	0.560	0.107	0.062	2.653
Egypt	Middle East and North Africa	4.283	0.045	4.371	4.195	9.367	0.750	61.998	0.749	-0.182	0.795	2.43	0.954	0.647	0.426	0.446	0.069	0.092	1.648
El Salvador	Latin America and Caribbean	6.061	0.065	6.188	5.933	9.054	0.762	66.402	0.888	-0.110	0.688	2.43	0.845	0.675	0.565	0.615	0.116	0.160	3.085
Estonia	Central and Eastern Europe	6.189	0.038	6.263	6.115	10.481	0.941	68.800	0.909	-0.106	0.527	2.43	1.344	1.079	0.640	0.641	0.119	0.263	2.103
Ethiopia	Sub-Saharan Africa	4.275	0.051	4.374	4.175	7.694	0.764	59.000	0.752	0.082	0.761	2.43	0.370	0.679	0.331	0.451	0.241	0.114	2.089
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
France	Western Europe	6.690	0.037	6.762	6.618	10.704	0.942	74.000	0.822	-0.147	0.571	2.43	1.421	1.081	0.804	0.536	0.092	0.235	2.521

In [59]:

countries

Out[59]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Algeria	Middle East and North Africa	4.887	0.053	4.991	4.783	9.342	0.802	66.005	0.480	-0.067	0.752	2.43	0.946	0.765	0.552	0.119	0.144	0.120	2.242
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Armenia	Commonwealth of Independent States	5.283	0.058	5.397	5.168	9.487	0.799	67.055	0.825	-0.168	0.629	2.43	0.996	0.758	0.585	0.540	0.079	0.198	2.127
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205

149 rows × 19 columns

In [60]:

countries.iloc[[0]]

Out[60]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.37	0.0	0.126	0.0	0.122	0.01	1.895

In [61]:

countries.iloc[[10,20,30,100]]

Out[61]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Belarus	Commonwealth of Independent States	5.534	0.047	5.625	5.442	9.853	0.910	66.253	0.650	-0.180	0.627	2.43	1.124	1.007	0.560	0.326	0.070	0.199	2.247
Cambodia	Southeast Asia	4.830	0.067	4.963	4.698	8.360	0.765	62.000	0.959	0.034	0.843	2.43	0.603	0.680	0.426	0.702	0.210	0.061	2.148
Croatia	Central and Eastern Europe	5.882	0.048	5.975	5.788	10.217	0.924	70.799	0.754	-0.118	0.939	2.43	1.251	1.039	0.703	0.453	0.111	0.000	2.325
Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736

In [62]:

countries.iloc[50:60]

Out[62]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Haiti	Latin America and Caribbean	3.615	0.173	3.953	3.276	7.477	0.540	55.700	0.593	0.422	0.721	2.43	0.294	0.173	0.227	0.257	0.463	0.139	2.060
Honduras	Latin America and Caribbean	5.919	0.082	6.081	5.758	8.648	0.812	67.300	0.857	0.081	0.809	2.43	0.703	0.787	0.593	0.578	0.241	0.083	2.934
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Hungary	Central and Eastern Europe	5.992	0.047	6.085	5.899	10.358	0.943	68.000	0.755	-0.186	0.876	2.43	1.301	1.083	0.615	0.454	0.067	0.040	2.432
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
India	South Asia	3.819	0.026	3.869	3.769	8.755	0.603	60.633	0.893	0.089	0.774	2.43	0.741	0.316	0.383	0.622	0.246	0.106	1.405
Indonesia	Southeast Asia	5.345	0.056	5.454	5.235	9.365	0.811	62.236	0.873	0.542	0.867	2.43	0.954	0.786	0.433	0.598	0.541	0.046	1.987
Iran	Middle East and North Africa	4.721	0.055	4.828	4.614	9.584	0.710	66.300	0.608	0.218	0.714	2.43	1.030	0.557	0.561	0.275	0.330	0.144	1.823
Iraq	Middle East and North Africa	4.854	0.059	4.970	4.738	9.240	0.746	60.583	0.630	-0.053	0.875	2.43	0.910	0.638	0.381	0.302	0.153	0.041	2.429
Ireland	Western Europe	7.085	0.040	7.164	7.006	11.342	0.947	72.400	0.879	0.077	0.363	2.43	1.644	1.092	0.753	0.606	0.238	0.367	2.384

In [63]:

houses.sort_index(ascending=False, inplace=True)

In [64]:

houses

Out[64]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287
21610	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	0	...	7	1020	0	2009	0	98144	47.5944	-122.299	1020	2007
21609	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	0	...	8	2310	0	2014	0	98146	47.5107	-122.362	1830	7200
21608	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	0	...	8	1530	0	2009	0	98103	47.6993	-122.346	1530	1509
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	0	...	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503
3	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	0	...	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
2	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	0	...	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
1	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	0	...	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
0	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	0	...	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650

21613 rows × 21 columns

In [65]:

houses.iloc[0:5]

Out[65]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287
21610	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	0	...	7	1020	0	2009	0	98144	47.5944	-122.299	1020	2007
21609	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	0	...	8	2310	0	2014	0	98146	47.5107	-122.362	1830	7200
21608	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	0	...	8	1530	0	2009	0	98103	47.6993	-122.346	1530	1509

5 rows × 21 columns

In [66]:

houses.loc[21612:21611]

Out[66]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287

2 rows × 21 columns

In [67]:

houses.loc[21612:21611, ['price', 'bedrooms']]

Out[67]:

	price	bedrooms
21612	325000.0	2
21611	400000.0	3

In [68]:

titanic.loc[50:60:2, ['name', 'sex', 'age']]

Out[68]:

	name	sex	age
50	Cardeza, Mrs. James Warburton Martinez (Charlo...	female	58
52	Carrau, Mr. Francisco M	male	28
54	Carter, Master. William Thornton II	male	11
56	Carter, Mr. William Ernest	male	36
58	Case, Mr. Howard Brown	male	49
60	Cavendish, Mr. Tyrell William	male	36

In [69]:

countries.loc["Canada": "Denmark", ['Ladder score']]

Out[69]:

	Ladder score
Country name
Canada	7.103
Chad	4.355
Chile	6.172
China	5.339
Colombia	6.012
Comoros	4.289
Congo (Brazzaville)	5.342
Costa Rica	7.069
Croatia	5.882
Cyprus	6.223
Czech Republic	6.965
Denmark	7.620

In [70]:

houses["bedrooms"].value_counts().loc[33]

Out[70]:

In [71]:

titanic["age"].value_counts().loc["18"]

Out[71]:

In [72]:

titanic["age"].value_counts().iloc[0:5]

Out[72]:

? 263

24 47

22 43

21 41

30 40

Name: age, dtype: int64